Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing (2101.02828v2)

Abstract: Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs). To evaluate AVs' safety performance unbiasedly, the probability distributions of environment statistics in the simulated naturalistic driving environment (NDE) need to be consistent with those from the real-world driving environment. However, although human driving behaviors have been extensively investigated in the transportation engineering field, most existing models were developed for traffic flow analysis without considering the distributional consistency of driving behaviors, which could cause significant evaluation biasedness for AV testing. To fill this research gap, a distributional consistent NDE modeling framework is proposed in this paper. Using large-scale naturalistic driving data, empirical distributions are obtained to construct the stochastic human driving behavior models under different conditions. To address the error accumulation problem during the simulation, an optimization-based method is further designed to refine the empirical behavior models. Specifically, the vehicle state evolution is modeled as a Markov chain and its stationary distribution is twisted to match the distribution from the real-world driving environment. The framework is evaluated in the case study of a multi-lane highway driving simulation, where the distributional accuracy of the generated NDE is validated and the safety performance of an AV model is effectively evaluated.

• The paper introduces a data-driven framework that models naturalistic driving behaviors to create realistic simulation environments for AV testing.
• The paper employs a Markov chain-based optimization technique to minimize long-term simulation errors by aligning simulated and empirical distributions.
• The paper's empirical validation on multi-lane highway scenarios demonstrates superior statistical fidelity compared to existing simulation models.

Overview of "Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing"

The paper introduces a novel framework to simulate Naturalistic Driving Environments (NDEs) for testing Autonomous Vehicles (AVs) with a focus on maintaining distributional consistency with real-world environments. This approach addresses the critical need for an accurate and unbiased evaluation of AV safety performance by ensuring that simulated driving conditions reflect the statistical properties of real-world driving.

Key Contributions

The paper makes several significant contributions to the field of AV testing:

1. NDE Modeling Framework: It proposes a data-driven framework that constructs driving behavior models using empirical distributions obtained from large-scale naturalistic driving data. This ensures that simulations reflect actual human driving behaviors with high fidelity.
2. Optimization for Error Reduction: An optimization-based approach is developed to address error accumulation during simulations. By modeling vehicle state evolution as a Markov chain, the method adjusts behavior models to align the stationary distribution of simulations with that of real-world data, minimizing long-term simulation errors.
3. Empirical Validation: The framework is evaluated in a multi-lane highway driving simulation. The results demonstrate that, compared to existing models, the proposed approach achieves superior distributional accuracy in the generated NDE.

Methodology

The paper integrates two principal components in its NDE modeling framework:

• Empirical Behavior Models: Using large-scale naturalistic driving datasets, the framework models driving behaviors such as free-driving and car-following through empirical action distributions under various conditions. This foundational step captures real-world stochastic behaviors in simulations.
• Error Minimization via Optimization: Recognizing that minor inaccuracies in behavior models can lead to substantial errors over time, the framework employs an optimization technique. Vehicle behaviors are modeled by Markov chains, and the respective stationarity constraints are adjusted to minimize divergence between simulated and empirical distributions.

Numerical Results and Implications

The NDE model demonstrates strong performance against various benchmarks. Notably, the proposed framework outperforms existing traffic simulators in replicating the statistical distribution of vehicle speeds and ranges, critical parameters for AV testing.

For AV testing, the generated NDE accurately reflects realistic driving conditions, enabling effective assessment of AV safety through crash rate estimation. This capability highlights the framework's potential in augmenting AV development and deployment processes by providing robust simulation environments that closely simulate real-world complexities.

Future Directions

The paper suggests several avenues for future exploration:

• Heterogeneous Vehicle Modeling: Upcoming research could explore extending the framework to accommodate different types of vehicles and driving styles, addressing the diversity observed in real-world urban settings.
• Scalability and Adaptation: Further studies might focus on adapting the framework to complex driving scenarios, such as urban intersections, dynamically involving pedestrians and cyclists.

Conclusion

This work presents a comprehensive framework for generating distributionally consistent simulations for AV testing, moving beyond the limitations of traditional models focused on traffic flow analysis. By ensuring that simulated environments faithfully reflect real-world statistics, the approach contributes substantively to advancing the field of autonomous driving safety evaluation.